Loss function plays a key role in self-supervised monocular depth estimation methods. Current reprojection loss functions are hand-designed and mainly focus on local patch similarity but overlook the global distribution differences between a synthetic image and a target image. In this paper, we leverage global distribution differences by introducing an adversarial loss into the training stage of self-supervised depth estimation. Specifically, we formulate this task as a novel view synthesis problem. We use a depth estimation module and a pose estimation module to form a generator, and then design a discriminator to learn the global distribution differences between real and synthetic images. With the learned global distribution differences, the adversarial loss can be back-propagated to the depth estimation module to improve its performance. Experiments on the KITTI dataset have demonstrated the effectiveness of the adversarial loss. The adversarial loss is further combined with the reprojection loss to achieve the state-of-the-art performance on the KITTI dataset.

中文翻译：

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Adv-Depth：具有对抗性损失的自监督单眼深度估计

损失函数在自我监督的单眼深度估计方法中起着关键作用。当前的重投影损失函数是手动设计的，并且主要关注局部斑块的相似性，但忽略了合成图像和目标图像之间的全局分布差异。在本文中，我们通过将对抗性损失引入自我监督深度估计的训练阶段来利用全局分布差异。具体来说，我们将此任务表述为一种新颖的视图合成问题。我们使用深度估计模块和姿势估计模块来构成生成器，然后设计一个鉴别器以了解真实图像与合成图像之间的全局分布差异。利用获悉的全局分布差异，可以将对抗损失反向传播到深度估计模块，以提高其性能。在KITTI数据集上进行的实验证明了对抗损失的有效性。将对抗性损失与重投影损失进一步结合，以在KITTI数据集上实现最新的性能。